Methods and systems for identifying anatomical landmarks in image data

ABSTRACT

Systems, devices, and methods are described for locating and identifying anatomical landmarks, such as ligament attachment points, in image data. These systems, devices, and methods may provide an oblique plane that contains an anatomical landmark such as a ligament attachment point to the tibia. For example, the position at which the anterior cruciate ligament (ACL), medial collateral ligament (MCL) posterior cruciate ligament (PCL), or patellar tendon attaches to the tibia may be identified. The systems, devices, and methods allow for tracing of an anatomical landmark to generate a 3-D marking on a 3-D surface model of a patient&#39;s bone. The attachment points may be useful landmarks for patient-matched instrumentation.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/950,756, filed on Mar. 10, 2014, which is hereby incorporated hereinby reference in its entirety.

BACKGROUND

Accurate location of anatomical landmarks, such as ligament attachmentpoints, in patient image data is critical to the overall outcome ofpatient-matched instrumentation techniques. Ligament attachment pointscan be used to facilitate consistent placement of implants on apatient's bone and may offer surgeons a reliable frame of reference forproperly orienting implants. However, the location of ligamentattachment points may not be known by a surgeon before a surgicaloperation and may need to be assessed intraoperatively. Noninvasiveimaging techniques, such as MRI scans and CT scans, allow image datarepresenting a patient's joint to be collected in advance of a surgicalprocedure. However, it can be difficult to use the raw image datacollected from preoperative imaging to provide a surgeon with accuratepreoperative information about the location of ligament attachmentpoints.

The location of anatomical landmarks in such image data is evaluatedusing 2-D slices within the orthogonal planes of a 3-D volume (e.g.,coronal, sagittal, and axial planes). This approach provides suboptimalvisualization of ligament attachment points that are not adequatelydepicted in any of these planes. As a result, identifying suchattachment points (e.g. attachments points on epicondyles) is atime-consuming process and suffers from high inter-operator variability.Therefore, to facilitate accurate preoperative location of ligamentattachment points, there is a need for improved visualization ofligament attachment points.

SUMMARY

Disclosed herein are systems, devices, and methods for locatinganatomical landmarks, such as ligament attachment points, in image data.In certain implementations, the systems, devices, and methods includereceiving image data, identifying a base oblique plane that is obliqueto the orthogonal planes in the image data, generating an adjusted setof orthogonal planes based on the base oblique plane, and generatingreformatted images sampled along the adjusted set of orthogonal planes.These systems, devices, and methods may provide an oblique plane thatmore adequately contains an anatomical landmark such as a ligamentattachment point to the tibia. In particular, the method may be used toidentify where the anterior cruciate ligament (ACL), medial collateralligament (MCL) posterior cruciate ligament (PCL), or patellar tendonattaches to the tibia. The systems, devices, and methods may allowtracing of an anatomical landmark in the reformatted image to generate a3-D marking on a 3-D surface model of a patient's bone. The attachmentpoints may be useful landmarks for patient-matched instrumentation. Thesystems, devices, and methods may also facilitate surgical planning. Forexample, the ligament locations may be used for preoperative planningand for designing patient-matched cutting blocks.

According to one aspect, a method for locating anatomical landmarks inimage data includes receiving, at a processor, image data representativeof a patient's joint, identifying an oblique plane intersecting a softtissue represented in the image data and normal to a longitudinal axisof the soft tissue, identifying, with the processor, a set of planesparallel or orthogonal to the oblique plane, generating, with theprocessor, a set of reformatted images, from the image data, sampledalong the set of planes, tracing a feature of the soft tissue in the setof reformatted images, and generating, with the processor, a 3-D markingrepresenting the traced feature on a 3-D model. In some implementations,the image data is raw image data. Tracing the feature may includetracing the feature in two or more reformatted images parallel to theoblique plane. Tracing the feature may include advancing throughparallel reformatted images selected from the set of reformatted images.Tracing the feature may include displaying a first reformatted image ona screen and displaying a second reformatted image on the screen,wherein the second reformatted image is parallel to and offset from thefirst reformatted image. Tracing the feature may include outlining across-section of the feature in a reformatted image from the set ofreformatted images. Tracing the feature may include storing datarepresenting the tracing in a matrix.

In certain implementations, generating the set of reformatted imagesincludes rotating the image data. Identifying the oblique plane mayinclude identifying a first point and a second point along thelongitudinal axis of the soft tissue, evaluating, with the processor, aline defined by the first and second points, and evaluating, with theprocessor, a plane normal to the line. The first point may be locatedabout where the soft tissue attaches to a patient's bone. The secondpoint may be located at about the center of the soft tissue. The firstpoint may be located at about the start of the soft tissue and thesecond point may be located at about the end of the soft tissue. Thelongitudinal axis of the soft tissue may be curved.

In certain implementations, identifying the oblique plane includesdrawing a curve substantially parallel to the longitudinal axis of thetissue, and evaluating, with the processor, an oblique plane normal tothe curve at the point where the oblique plane intersects the curve. Incertain implementations, a plurality of reformatted images is generated,each reformatted image being normal to the curve at the point where eachreformatted image intersects the curve. Generating the 3-D marking mayinclude generating a 3-D surface model from the matrix. In certainimplementations, the method further includes smoothing the 3-D marking.The 3-D model may be a model of a patient's bone. The 3-D model may be amodel of a patient's tibia. The 3-D model may be displayed with theimage data to orient a user.

In certain implementations, generating the set of reformatted imagesincludes identifying, with the processor, four corners of the obliqueplane, generating, with the processor, texture coordinates using thecorner locations, and interpolating, with the processor, pixelintensities for the texture coordinates using the image data. The softtissue may be an ACL, a MCL, a PCL, or a patellar tendon of a patient.

According to one aspect, a system for locating anatomical landmarks inimage data includes means for receiving image data representative of apatient's joint, means for identifying an oblique plane intersecting asoft tissue represented in the image data and normal to a longitudinalaxis of the soft tissue, means for identifying a set of planes parallelor orthogonal to the oblique plane, means for generating a set ofreformatted images, from the image data, sampled along the set ofplanes, means for tracing a feature of the soft tissue in the set ofreformatted images, and means for generating a 3-D marking representingthe traced feature on a 3-D model. In some implementations, the imagedata is raw image data. The means for tracing the feature may includemeans for tracing the feature in two or more reformatted images parallelto the oblique plane. The means for tracing the feature may includemeans for advancing through parallel reformatted images selected fromthe set of reformatted images.

In certain implementations, the means for tracing the feature includesmeans for outlining a cross-section of the feature in a reformattedimage from the set of reformatted images. The means for tracing thefeature may include means for storing data representing the tracing in amatrix. The means for generating the set of reformatted images mayinclude means for rotating the image data. The means for identifying theoblique plane may include means for identifying a first point and asecond point along the longitudinal axis of the soft tissue, means forevaluating a line defined by the first and second points, and means forevaluating a plane normal to the line. The first point may be locatedabout where the soft tissue attaches to a patient's bone. The secondpoint may be located at about the center of the soft tissue. The firstpoint may be located at about the start of the soft tissue and thesecond point may be located at about the end of the soft tissue. Thelongitudinal axis of the soft tissue may be curved.

In certain implementations, the means for identifying the oblique planeincludes means for drawing a curve substantially parallel to thelongitudinal axis of the tissue, and means for evaluating an obliqueplane normal to the curve at the point where the oblique planeintersects the curve. In certain implementations a plurality ofreformatted images is generated, each reformatted image being normal tothe curve at the point where each reformatted image intersects thecurve. The means for generating the 3-D marking may include means forgenerating a 3-D surface model from the matrix. In certainimplementations, the system further includes means for smoothing the 3-Dmarking.

In certain implementations, the 3-D model is a model of a patient'sbone. The 3-D model may be a model of a patient's tibia. The 3-D modelmay be displayed with the image data to orient a user. In certainimplementations, the means for generating the set of reformatted imagesincludes means for computing four corners of the oblique plane, meansfor generating texture coordinates using the corner locations, and meansfor interpolating pixel intensities for the texture coordinates usingthe image data. In certain implementations the soft tissue is an ACL, aMCL, a PCL, or a patellar tendon of a patient.

According to one aspect, a method for locating anatomical landmarks inimage data, includes receiving, at a processor, image datarepresentative of a patient's joint, identifying an oblique planeintersecting a soft tissue represented in the image data and normal to alongitudinal axis of the soft tissue, generating, with the processor, areformatted image, from the image data, sampled along the oblique plane,tracing a feature of the soft tissue in the reformatted image, andgenerating, with the processor, a 3-D marking representing the tracedfeature on a 3-D model. In some implementations, the image data is rawimage data.

Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative flowchart for identifying anatomicallandmarks in image data;

FIG. 2 shows an illustrative flowchart for identifying a base obliqueplane;

FIG. 3 shows an illustrative graphical user interface for identifyingthe base oblique plane of FIG. 2;

FIG. 4A shows an illustrative graphical user interface for identifying afirst point and a second point to identify a line parallel to alongitudinal axis of soft tissue;

FIG. 4B shows an illustrative preview window that displays a candidatebase oblique plane;

FIG. 4C shows an illustrative graphical user interface for identifying acurve parallel to a longitudinal axis of soft tissue;

FIG. 5 shows an illustrative flowchart for generating reformatted imagessampled along an adjusted set of orthogonal planes;

FIGS. 6A and 6B show screenshots of an illustrative graphical userinterface displaying reformatted images sampled along the adjusted setof orthogonal planes;

FIG. 7 shows an illustrative graphical user interface for performing atracing process using multiple views provided by the adjusted set oforthogonal planes; and

FIG. 8 shows illustrative 3-D markings generated from tracing data.

DETAILED DESCRIPTION

To provide an overall understanding of the systems, devices, and methodsdescribed herein, certain illustrative embodiments will be described.Although the embodiments and features described herein are specificallydescribed for use in connection with identifying anatomical landmarks inimage data for a patient's knee joint, it will be understood that theapproaches disclosed are applicable to other anatomical joints as well.Moreover, the approaches outlined below may be applied to preparingpatient-matched medical devices and implants to be used in surgicalprocedures including, but not limited to, knee, acetabular, spinearthroplasty, cranio-maxillofacial surgical procedures, shoulderarthroplasty, as well as foot, ankle, hand, and other extremityprocedures.

The following disclosure provides systems, devices, and methods foridentifying anatomical landmarks in image data. Various anatomicallandmarks, such as ligament attachment points to the tibia, may belocated in the image data using a set of reformatted images that providea customized view of an anatomical landmark. It will be understood thatthe set of reformatted images may include two or more reformattedimages. A ligament attachment point for which this approach iswell-suited is the attachment point between the anterior cruciateligament (ACL) and the tibia. Any other suitable soft tissue, softtissue attachment points, or other features of a patient's joint or bonemay be identified including, for example, the medial sulcus of the tibiaplateau or the lateral sulcus of the tibia plateau as well as attachmentpoints between the medial collateral ligament (MCL) and the tibia,between the posterior cruciate ligament (PCL) and the tibia, between thepatellar tendon and the tibia, or any other suitable feature orlandmark.

FIG. 1 shows a flowchart 100 for identifying anatomical landmarks inimage data according to certain embodiments. In step 102, image datarepresentative of a patient's joint is received. The representation ofthe patient's joint includes soft tissue, such as a ligament, and bone.The soft tissue (e.g. ligament) attaches to the bone at an attachmentpoint. Accurately identifying such an attachment point on a patient'sbone provides surgeons with accurate preoperative information about thepatient's anatomy and accurate models of a patient's joint forpatient-matched instrumentation. The image data received in step 102 canbe raw image data. For example, the image data may be received from animaging machine without significant post-processing. The image data canbe obtained using any suitable medical imaging technique including, forexample, MRI, CT scan, ultrasound, X-ray, or any other suitabletechnique or combination thereof. The image data may be topographicimage data comprising multiple image slices taken in orthogonal planes.Orthogonal planes for tomographic imaging may include medial-lateralplanes (i.e. sagittal planes), anterior-posterior planes (i.e. coronalplanes), and transverse planes (i.e. axial planes). These views,however, may not provide the best available view for identifying aparticular ligament attachment point. To create a customized set ofviews that facilitate landmark identification, a user can identify abase oblique plane, oblique to one or more of the orthogonal planes instep 104. The customized set of views may include two or more views. Thebase oblique plane identified in step 104 is normal to the longitudinalaxis of the soft tissue and intersects the soft tissue. Various methodsmay be used to identify a base oblique plane and arc discussed in detailin relation to FIGS. 2-4.

After the base oblique plane is identified, an adjusted set oforthogonal planes that are orthogonal to the base oblique plane isgenerated in step 105. The adjusted set of orthogonal planes may provideviews that effectively rotate the coronal, sagittal, and axial views. Anadjusted set of orthogonal planes may be, for example, two or moreplanes, including the base plane. In step 106, reformatted images aresampled along the adjusted set of orthogonal planes generated in step105. The reformatted images provide a customized set of views thatfacilitate identification of soft tissue attachment points on apatient's bone. Additionally, the reformatted images allow the landmarkidentification to be performed without relying on cross-sectional viewsof a 3-D model generated from segmentation of the image data. As aresult, errors associated with 3-D model generation (e.g. segmentationerrors and smoothing errors) are not introduced into the landmarkidentification process. Thus, accuracy may be increased by limiting thenumber of processing steps applied to the image data prior to landmarkidentification.

The generation of the reformatted images is further described inrelation to FIG. 5 and an illustrative graphical user interfacedisplaying a set of reformatted images is shown in FIGS. 6B and 7. Thegeneration of the reformatted images may be performed by a graphicsprocessing unit (GPU) to reduce processing time, thereby allowingdynamic, “on-the-fly,” generation of views more conducive to landmarkidentification. While a central processing unit (CPU) may also be used,a GPU may be more computationally efficient due to the highly parallelarchitecture of GPUs. Additionally, generating the reformatted imagesalong the adjusted set of orthogonal planes does not necessarily requirethe processing capabilities needed to form 3-D models of the joint. Oncethe reformatted images have been generated, a user traces a feature ofthe soft tissue in one or more reformatted images in step 108. Theprocess for tracing the soft tissue feature is further described inrelation to FIG. 7. Because the user traces this feature fromreformatted image data without relying on cross-sectional views of 3-Dmodels generated from segmentation data, segmentation errors andsmoothing errors associated with 3-D model generation may be avoided. Instep 110, a computer processor uses the tracings to generate a 3-Dmarking representing the traced feature on a 3-D model. If desired, the3-D marking can be smoothed as further described in relation to FIG. 8.

FIG. 2 shows a flowchart 200 for identifying a base oblique planeaccording to certain embodiments. For example, the steps of flowchart200 are related to step 104 of process 100. The steps of process 200 maybe performed with reference to the graphical user interface of FIGS. 3and 4A. In step 202, the user identifies a first point and a secondpoint along the longitudinal axis of the soft tissue. The first andsecond points may be identified in a first orthogonal slice of raw imagedata. For identifying ligament attachment points in the knee joint, theorthogonal plane used may be a medial-lateral plane. For example, asshown in FIG. 4A, a first point 410 a and a second point 410 b areselected in a medial-lateral plane. It may be desirable to locate thefirst point about where the soft tissue attaches to a patient's bonebecause the first point may be used later to position a base obliqueplane that provides a top view of the attachment point. The second pointmay be located at about the center of the soft tissue. Such a placementof the first point 410 a and the second point 410 b is shown in FIG. 4A.

In step 204, the processor evaluates a line defined by the first andsecond points. For example, as shown in FIG. 4A, line 412 is evaluatedbased on the location of point 410 a and point 410 b. The line is thenused in step 206 to evaluate a base oblique plane normal to the line.For any given line there is a set of many planes normal to the line andparallel to each other along the length of the line. Therefore, in step208 one of the planes from the set of planes is identified as the baseoblique plane. In some embodiments, the selected base oblique plane isthe plane from the set of planes that contains the first point. Forexample, in FIG. 4B, the selected base oblique plane 424 contains firstpoint 410 a. Any other suitable plane from the set of planes thatadequately shows the soft tissue attachment point may be selected.

FIG. 4C shows a graphical user interface for identifying a curveparallel to a longitudinal axis of soft tissue according to certainembodiments. Because the longitudinal axis of the soft tissue may becurved, a user may be able to draw a curve parallel to the longitudinalaxis of the soft tissue instead of a line. When a line is drawn todefine the base oblique plane, planes offset from the oblique plane willalso be normal to the line. However, when a curve is used to define thebase oblique plane, planes offset from that base oblique plane may insome cases not be normal to the curve at the point that the offset planeintersects the curve. Therefore, to better align the adjusted set oforthogonal planes to the curve, multiple base oblique planes may begenerated, each base oblique plane being normal to the curve at thepoint that each plane intersects the curve.

FIG. 3 shows a graphical user interface 300 for identifying the baseoblique plane of FIG. 2 according to certain embodiments. Graphical userinterface 300 contains an image window 302, and buttons 306, 308, and310. The image window displays an orthogonal slice of raw image data. Inthis example, a medial-lateral slice of a patient's knee joint is shown.Located near the center of the knee joint is a soft tissue 304. The softtissue shown in the window is the anterior cruciate ligament (ACL). TheACL is a narrow soft tissue structure which attaches to the tibia andthe femur. The ACL may not be adequately contained in the orthogonalplanes. For better visualization of the ACL and its attachment points,an adjusted set of orthogonal views based on a base oblique plane thatintersects the ACL and is normal to the longitudinal axis of the ACL isdesired.

Before selecting the first and second points to evaluate the baseoblique plane, the image quality may be assessed. Next case button 306is provided so that a case can be skipped if the image quality is foundto be inadequate for analysis. An image can be inadequate for a numberof reasons such as low image resolution, poor visibility of thestructures to be identified, or absence of segmentation data. Thegraphical user interface may provide the user with the ability toannotate an inadequate image to note the reason for skipping it. If,however, the image quality is found to be acceptable, the user can press“set rotation” button 308 to initiate the process of identifying anappropriate oblique plane as further described in FIG. 4A. Save button310 is provided so that users can save their progress at any pointduring the landmark identification process.

FIG. 4A shows a graphical user interface for identifying a first pointand a second point to identify a line parallel to a longitudinal axis ofsoft tissue according to certain embodiments. After the user has pressedbutton 308, line 412 and points 410 a and 410 b appear on the graphicaluser interface. The user can drag and drop the points 410 a and 410 b toalign line 412 with the longitudinal axis of the soft tissue 304. It maybe desirable for line 412 to be placed on the anterior side of the ACL.The first point 410 a may be selected to coincide with the attachmentpoint of the ACL, and the second point 410 b may be selected to coincidewith the midpoint of the ACL although any suitable point along the ACLmay be selected. The first point 410 a may be used later to select aplane from a set of planes that are normal to line 412. The second point410 b may be used primarily for establishing the slope of line 412. Insuch an implementation, the exact position of point 410 a may be lessimportant than the position of point 410 b or the slope of line 412.That is, the direction of the line may be more important than itslength.

FIG. 4B shows a preview window 420 that displays a candidate baseoblique plane 424 according to certain embodiments. The candidate baseoblique plane 424 provides a preview of the base oblique plane thatwould be generated from the user's current selection of points 410 a and410 b. The candidate base oblique plane 424 may be shown in the previewwindow 420 while the user positions the points 410 a and 410 b. Such apreview window 420 may aid the user in the process of identifying thefirst and second points. The candidate base oblique plane is the obliqueplane selected from the set of oblique planes normal to line 412 thatcontains first point 410 a.

The preview window 420 also displays a reformatted image on thecandidate base oblique plane 424. The user may accept or reject thecandidate base oblique plane based on whether the reformatted imageprovides an adequate view of a ligament attachment point. Thereformatted image on the candidate base oblique plane 424 may begenerated using a graphics processing unit (GPU) to reduce processingtime. Additionally, the preview window 410 displays a 3-D view of boththe candidate base oblique plane 424 and an orthogonal slice 422together so that the user can verify the relative orientation of thecandidate base oblique plane. A 3-D model of the patient's bone 428 mayalso be displayed with the image data to orient the user. If thecandidate base oblique plane 424 is found to be inadequate, the user mayreposition points 410 a and 410 b to generate a new candidate baseoblique plane 424. If the candidate base oblique plane 424 is found tobe acceptable, the user may indicate the acceptance of the plane byclicking a button, and a separate window displaying the reformattedimage may appear in the graphical user interface. Example reformattedimage windows are shown in FIGS. 6B and 7.

FIG. 5 shows a flowchart 500 for generating reformatted images sampledalong the adjusted set of orthogonal planes according to certainembodiments. For example, the steps of flowchart 500 are related to step106 of process 100. In step 502, the location and orientation of thebase oblique plane are received. This location and orientationinformation defines the base oblique plane. An adjusted set oforthogonal planes may be generated based on the base oblique plane. Eachplane in the adjusted set of orthogonal planes is either parallel ororthogonal to the base oblique plane. The oblique planes in the set maybe equally spaced and may span the entire volume of the raw image data.Therefore, when the reformatted images are sampled along the obliqueplanes, the raw image data may be effectively rotated, resulting in anew stack of image slices oriented with the base oblique plane. In thecase of the oblique planes generated along a curve, there may bemultiple base oblique planes. In such a case, planes orthogonal to eachbase oblique plane may be generated to provide a set of adjustedorthogonal views that follows the curve. It is also envisioned that thesteps here can be performed without identifying a set of orthogonalplanes. In such embodiments, a reformatted image may be sampled alongthe base oblique plane only.

Four corners of the planes in the adjusted set of planes are computed instep 504. The corners of the oblique planes are determined by theintersection of the infinite plane and the bounding volume of the rawimage data. For example, as shown in FIG. 4A, candidate base obliqueplane 424 has corner 426 at the intersection of the oblique plane andthe bounding volume of the raw image data. In step 506, the corners areused to generate texture coordinates for the planes in the adjusted setof orthogonal planes. The texture coordinates are given pixel intensityvalues for displaying the reformatted image on the oblique plane. Pixelintensity values may represent, for example, radiodensity measurements,attenuation coefficients, or tissue relaxation time such as T1, T2, andproton density. In the texture mapping process of step 508, the pixelintensity values for texture coordinates that do not coincide withpoints in the raw image data are inferred using interpolation. Possibleinterpolation methods include nearest-neighbor interpolation, bilinearinterpolation, trilinear interpolation, cubic interpolation, and anyother suitable technique for interpolation or any combination thereof.One of the differences among the types of interpolation is theassumption of the continuity of the spatial distribution of the pixelvalues. Linear interpolation, for example, assumes the intensity varieslinearly so the unknown value can be determined from its four neighborsas a weighted average. Cubic interpolation, on the other hand, has ahigher requirement on the continuity: not only the intensity value butalso the first and second order derivatives must be continuous. Cubicinterpolation thus requires more neighboring points beyond the immediatefour neighbors, but produces results that are smoother. Step 508 may beperformed using the graphical processing unit (GPU) or the centralprocessing unit (CPU). Using the GPU instead of the CPU may haveperformance benefits due to the highly parallel architecture of GPUs.The output of the interpolation step may be a matrix of pixel intensityvalues, with each value in the matrix corresponding to a generatedtexture coordinate. The GPU or the CPU sends the output to the displayenvironment in step 510.

FIGS. 6A and 6B show screenshots of a graphical user interfacedisplaying reformatted images sampled along the adjusted set oforthogonal planes. Window 606 shows the reformatted image on the baseoblique plane. The image shown in window 606 is the same reformed imagethat was previewed on candidate base oblique plane 424. Windows 602 aand 604 show the corresponding views orthogonal to the base obliqueplane. Therefore, windows 602 a, 604 and 606 together provide a set ofadjusted orthogonal views. Thus, by generating reformatted images alongthe adjusted set of orthogonal planes, the raw image data is effectivelyrotated to provide a new stack of image slices oriented with respect tothe base oblique plane. The user may be able to advance through thereformatted images in the same way that the user would normally advancethrough orthogonal slices of the raw image data. Additionally, the usermay be provided with a tool bar having toggle buttons that allowdifferent views to be turned on or off.

FIG. 7 shows a graphical user interface for performing a tracing processusing multiple views provided by the adjusted set of orthogonal planesaccording to certain embodiments. Reformatted image window 708 shows anenlarged reformatted image of a patient's knee joint and 2-D marking 710c. 2-D markings may be created by tracing the ACL attachment point inimage windows 702, 704, or 708. For example, 2-D marking 710 c may becreated by tracing the attachment point in image window 708. The 2-Dmarkings may be traced using a cursor controlled by computer mouse. Forexample, a user may click and hold a mouse button while tracing theligament attachment point with the cursor to create the 2-D marking.Additionally, a user may advance through reformatted images duringtracing. For example, the user may advance through reformatted imagesnormal to the longitudinal axis of the soft tissue beginning just beforethe ligament attachment point and may stop after the end of theligament. Advancing through images in this way may facilitateidentification of the end of a ligament, and therefore the ligamentattachment point, because there may be a sharp change in the grayscaleof the cross-sections as the end of the ligament is passed.Alternatively, a user may start just before the starting point of aligament and advance through reformatted images until the end of theligament is reached or just passed. Advancing in this way may allow userto identify attachment points on both ends of the ligament. For example,advancing from the start of the ACL to its end may facilitateidentification of both the attachment point to the femur and theattachment point to the tibia.

As the user advances through reformatted images, the user may outlinethe cross-sections of a ligament in any of the adjusted orthogonalplanes. Additionally, a user may also be provided with the ability toerase all or a part of a 2-D marking made in any view. It may bedesirable to advance through 8 to 10 reformatted images during tracingto adequately mark an attachment point. As the user traces theattachment point in the cross-sections, a matrix representing thetracing data may be generated. For example, the data representing thepoints selected during tracing may be stored in a binary map. Thistracing data may be used to generate a 3-D marking as discussed furtherin relation to FIG. 8.

The graphical user interface of FIG. 7 also contains surface modelwindow 706 which displays a 3-D surface model 712 representing apatient's tibia. The 3-D surface model may help orient the user to thepatient's anatomy during tracing. The 3-D surface model may be createdprior to tracing based on segmentation of the raw image data. The same2-D marking traced in the adjusted orthogonal planes is displayed asmarking 710 b on the 3-D surface model 712. Thus, the graphical userinterface may facilitate creation of 2-D markings representing theligament attachment point.

FIG. 8 shows 3-D markings generated from tracing data according tocertain embodiments. The 3-D markings may be generated using the 2-Dmarkings created in the tracing process described in FIG. 7. Forexample, 3-D marking 802, which represents the ACL attachment point tothe tibia, is generated from 2-D markings 710 a and 710 c. In certainimplementations, 3-D marking 802 may be generated from the tracing datausing the marching cubes algorithm. 3-D marking 804, which representsthe patellar tendon attachment point to the anterior side of the tibia,can be created by a similar process as that used to create 3-D marking802. Alternatively, point markings 806 and 808 may each be created fromthe identification of a single landmark point in one or more of theadjusted orthogonal planes. The point marking 806 is selected tocorrespond to the medial sulcus of the tibia plateau and point marking808 is selected to correspond to the lateral sulcus of the tibiaplateau. The point markings are selected to correspond to the lowestpoints on the tibia condyles. Additionally, smoothing may be performedon the surface model or on the tracing data before generation of thesurface model. Such smoothing may be performed for processing efficiencybecause smoothed 3-D markings may not require subsequent smoothing whenimported into a computer aided design (CAD) environment. Thus, thetracing process described in relation to FIG. 7 may be used to generate3-D markings representing anatomical landmarks, such as ligamentattachment points.

The foregoing is merely illustrative of the principles of thedisclosure, and the systems, devices, and methods can be practiced byother than the described embodiments, which are presented for purposesof illustration and not of limitation. It is to be understood that thesystems, devices, and methods disclosed herein, while shown for use inknee arthroplasty systems, may be applied to systems, devices, andmethods to be used in other surgical procedures including, but notlimited to, acetabular, spine arthroplasty, cranio-maxillofacialsurgical procedures, shoulder arthroplasty, as well as foot, ankle,hand, and extremities procedures.

Example Embodiments

A1. A method for locating anatomical landmarks in image data,comprising:

receiving, at a processor, image data representative of a patient'sjoint;

identifying an oblique plane intersecting a soft tissue represented inthe image data and normal to a longitudinal axis of the soft tissue;

identifying, with the processor, a set of planes comprising the obliqueplane and at least one other plane parallel or orthogonal to the obliqueplane;

generating, with the processor, a set of reformatted images, from theimage data, sampled along the set of planes;

tracing a feature of the soft tissue in the set of reformatted images;and

generating, with the processor, a 3-D marking representing the tracedfeature on a 3-D model.

A2. The method of embodiment A1, wherein tracing the feature comprisestracing the feature in two or more reformatted images parallel to theoblique plane.

A3. The method of embodiment A1 or A2, wherein tracing the featurecomprises:

displaying a first reformatted image on a screen; and

displaying a second reformatted image on the screen, wherein the secondreformatted image is parallel to and offset from the first reformattedimage.

A4. The method of any of embodiments A1-A3, wherein tracing the featurecomprises outlining a cross-section of the feature in a reformattedimage from the set of reformatted images.

A5. The method of any of embodiments A1-A4, wherein tracing the featurecomprises storing data representing the tracing in a matrix.

A6. The method of any of embodiments A1-A5, wherein generating the setof reformatted images comprises rotating the image data.

A7. The method of any of embodiments A1-A6, wherein identifying theoblique plane comprises:

identifying a first point and a second point along the longitudinal axisof the soft tissue;

evaluating, with the processor, a line defined by the first and secondpoints; and

evaluating, with the processor, a plane normal to the line.

A8. The method of any of embodiments A1-A7, wherein the first point islocated about where the soft tissue attaches to a patient's bone.

A9. The method of any of embodiments A1-A8, wherein the second point islocated at about the center of the soft tissue.

A10. The method of any of embodiments A1-A7, wherein the first point islocated at about the start of the soft tissue and the second point islocated at about the end of the soft tissue.

A11. The method of any of embodiments A1-A10, wherein the longitudinalaxis of the soft tissue is curved.

A12. The method of embodiment A11, wherein identifying the oblique planecomprises:

drawing a curve substantially parallel to the longitudinal axis of thetissue; and

evaluating, with the processor, an oblique plane normal to the curve atthe point where the oblique plane intersects the curve.

A13. The method of embodiment A1l or A12, wherein a plurality ofreformatted images is generated, each reformatted image being normal tothe curve at the point where each reformatted image intersects thecurve.

A14. The method of any of embodiments A1-A13, wherein generating the 3-Dmarking comprises generating a 3-D surface model from the matrix.

A15. The method of any of embodiments A1-A14, further comprisingsmoothing the 3-D marking.

A16. The method of any of embodiments A1-A15, wherein the 3-D model is amodel of a patient's bone.

A17. The method of any of embodiments A1-A16, wherein the 3-D model is amodel of a patient's tibia.

A18. The method of any of embodiments A1-A17 wherein the 3-D model isdisplayed with the image data to orient a user.

A19. The method of any of embodiments A1-A18, wherein generating the setof reformatted images comprises:

identifying, with the processor, four corners of the oblique plane;

generating, with the processor, texture coordinates using the cornerlocations; and

interpolating, with the processor, pixel intensities for the texturecoordinates using the image data.

A20. The method of any of embodiments A1-A19, wherein the soft tissue isan ACL, a MCL, a PCL, or a patellar tendon of a patient.

B1. A system for locating anatomical landmarks in image data,comprising:

means for receiving image data representative of a patient's joint;

means for identifying an oblique plane intersecting a soft tissuerepresented in the image data and normal to a longitudinal axis of thesoft tissue;

means for identifying a set of planes parallel or orthogonal to theoblique plane;

means for generating a set of reformatted images, from the image data,sampled along the set of planes;

means for tracing a feature of the soft tissue in the set of reformattedimages; and

means for generating a 3-D marking representing the traced feature on a3-D model.

B2. The system of embodiment B1, wherein the means for tracing thefeature comprises means for tracing the feature in two or morereformatted images parallel to the oblique plane.

B3. The system of embodiment B1 or B2, wherein the means for tracing thefeature comprises means for advancing through parallel reformattedimages selected from the set of reformatted images.

B4. The system of any of embodiments B1-B3, wherein the means fortracing the feature comprises means for outlining a cross-section of thefeature in a reformatted image from the set of reformatted images.

B5. The system of any of embodiments B1-B4, wherein the means fortracing the feature comprises means for storing data representing thetracing in a matrix.

B6. The system of any of embodiments B1-B5, wherein the means forgenerating the set of reformatted images comprises means for rotatingthe image data.

B7. The system of any of embodiments B1-B6, wherein the means foridentifying the oblique plane comprises:

means for identifying a first point and a second point along thelongitudinal axis of the soft tissue;

means for evaluating a line defined by the first and second points; and

means for evaluating a plane normal to the line.

B8. The system of any of embodiments B1-B7, wherein the first point islocated about where the soft tissue attaches to a patient's bone.

B9. The system of any of embodiments B1-B8, wherein the second point islocated at about the center of the soft tissue.

B10. The system of any of embodiments B1-B7, wherein the first point islocated at about the start of the soft tissue and the second point islocated at about the end of the soft tissue.

B11. The system of any of embodiments B1-B10, wherein the longitudinalaxis of the soft tissue is curved.

B12. The system of embodiment B11, wherein the means for identifying theoblique plane comprises:

means for drawing a curve substantially parallel to the longitudinalaxis of the tissue; and

means for evaluating an oblique plane normal to the curve at the pointwhere the oblique plane intersects the curve.

B13. The system of embodiment B11 or B12, wherein a plurality ofreformatted images is generated, each reformatted image being normal tothe curve at the point where each reformatted image intersects thecurve.

B14. The system of any of embodiments B1-B13, wherein the means forgenerating the 3-D marking comprises means for generating a 3-D surfacemodel from the matrix.

B15. The system of any of embodiments B1-B14, further comprising meansfor smoothing the 3-D marking.

B16. The system of any of embodiments B1-B15, wherein the 3-D model is amodel of a patient's bone.

B17. The system of any of embodiments B1-B16, wherein the 3-D model is amodel of a patient's tibia.

B18. The system of any of embodiments B1-B17 wherein the 3-D model isdisplayed with the image data to orient a user.

B19. The system of any of embodiments B1-B18, wherein the means forgenerating the set of reformatted images comprises:

means for computing four corners of the oblique plane;

means for generating texture coordinates using the corner locations; and

means for interpolating pixel intensities for the texture coordinatesusing the image data.

B20. The system of any of embodiments B1-B19, wherein the soft tissue isan ACL, a MCL, a PCL, or a patellar tendon of a patient.

C1. A method for locating anatomical landmarks in image data,comprising:

receiving, at a processor, image data representative of a patient'sjoint;

identifying an oblique plane intersecting a soft tissue represented inthe image data and normal to a longitudinal axis of the soft tissue;

generating, with the processor, a reformatted image, from the imagedata, sampled along the oblique plane;

tracing a feature of the soft tissue in the reformatted image; and

generating, with the processor, a 3-D marking representing the tracedfeature on a 3-D model.

Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented.

Examples of changes, substitutions, and alterations are ascertainable byone skilled in the art and could be made without departing from thescope of the information disclosed herein. All references cited hereinare incorporated by reference in their entirety and made part of thisapplication.

1-15. (canceled)
 16. A system for locating anatomical landmarks in imagedata, comprising: a device configured to receive image datarepresentative of a patient's joint; one or more graphical userinterface units configured to: receive user inputs identifying a firstpoint and a second point along a portion of soft tissue of the patientrepresented in the image data representative of the patient's joint;receive user inputs tracing a feature of the portion of soft tissue ofthe patient having a longitudinal axis; one or more processors coupledto the device configured to receive image data representative of apatient's joint and the one or more graphical user interface unitsconfigured to: identify one of a line or a curve that connects the firstpoint and the second point along a portion of soft tissue of thepatient; identify a set of planes comprising (i) an oblique planeintersecting a normal to the line or the curve and (ii) at least oneother plane parallel to the oblique plane; generate, using the imagedata representative of the patient's joint, a set of reformatted images;generate a 3-D model representative of the patient's joint using the setof reformatted images; generate a 3-D marking representing the tracedfeature of the portion of soft tissue of the patient on the 3-D modelrepresentative of the patient's joint; and one or more screens coupledto the one or more graphical user interface units and the one or moreprocessors configured to display one or more of the image datarepresentative of the patient's joint, the first point and the secondpoint along the portion of soft tissue of the patient represented in theimage data representative of the patient's joint, the traced feature ofthe portion of soft tissue of the patient, the oblique plane, the 3-Dmodel representative of the patient's joint, one or more reformattedimages from the set of reformatted images, and the 3-D markingrepresenting the traced feature of the portion of soft tissue of thepatient.
 17. The system of claim 16, wherein the image datarepresentative of the patient's joint comprises image slices taken inparallel image planes selected from one of sagittal planes, coronalplanes, or axial planes of the patient.
 18. The system of claim 17,wherein the oblique plane is parallel to the parallel image planes. 19.The system of claim 16, wherein receiving user inputs tracing thefeature of the portion of soft tissue of the patient comprises receivinguser inputs tracing the feature in two or more reformatted images fromthe set of reformatted images.
 20. The system of claim 16, whereinreceiving user inputs tracing the feature of the portion of soft tissueof the patient comprises: displaying on the one or more graphical userinterface units a first reformatted image from the set of reformattedimages; and displaying on the one or more graphical user interface unitsa second reformatted image from the set of reformatted images, whereinthe second reformatted image is parallel to and offset from the firstreformatted image.
 21. The system of claim 16, wherein the 3-D model ofthe patient's joint is generated using the image data of the patient'sjoint prior to tracing the feature of the portion of soft tissue of thepatient.
 22. The system of claim 16, wherein receiving user inputstracing the feature of the soft tissue of the patient comprises storingdata representing the tracing in a matrix.
 23. The system of claim 16,wherein receiving user inputs tracing the feature of the soft tissue ofthe patient comprises outlining a cross-section of the feature of thesoft tissue of the patient one or more reformatted images from the setof reformatted images.
 24. The system of claim 16, wherein generatingthe set of reformatted images comprises sampling the image datarepresentative of the patient's joint along the oblique plane.
 25. Thesystem of claim 16, wherein the one or more graphical user interfaceunits further comprise one or more pointing devices, including a mouse.26. The system of claim 16, wherein the first point along the portion ofsoft tissue of the patient is an attachment point of the soft tissue ofthe patient to the patient's bone and the second point along the portionof soft tissue of the patient is a midpoint of the soft tissue of thepatient.
 27. The system of claim 16, wherein the soft tissue of thepatient is a patellar tendon of a patient.
 28. A system for locatinganatomical landmarks in image data, comprising: a device configured togenerate image data representative of a patient's joint; one or moregraphical user interface units configured to: receive user inputsidentifying a first point and a second point along a portion of softtissue of the patient represented in the image data representative ofthe patient's joint; receive user inputs tracing a feature of theportion of soft tissue of the patient having a longitudinal axis that iscurved; one or more processors coupled to the device configured toreceive image data representative of a patient's joint and the one ormore graphical user interface units configured to: identify one of aline or a curve that connects the first point and the second point alonga portion of soft tissue of the patient; identify a set of planescomprising (i) an oblique plane intersecting a normal to the line or thecurve and (ii) at least one other plane parallel to the oblique plane;generate, using the image data representative of the patient's joint, aset of reformatted images; generate a 3-D model representative of thepatient's joint using the set of reformatted images; generate a 3-Dmarking representing the traced feature of the portion of soft tissue ofthe patient on the 3-D model representative of the patient's joint; andone or more screens coupled to the one or more graphical user interfaceunits and the one or more processors configured to display one or moreof the image data representative of the patient's joint, the first pointand the second point along the portion of soft tissue of the patientrepresented in the image data representative of the patient's joint, thetraced feature of the portion of soft tissue of the patient, the obliqueplane, the 3-D model representative of the patient's joint, one or morereformatted images from the set of reformatted images, and the 3-Dmarking representing the traced feature of the portion of soft tissue ofthe patient.
 29. The system of claim 28, wherein identifying the obliqueplane comprises: receiving, at the one or more graphical userinterfaces, a user input identifying a curve substantially parallel tothe longitudinal axis of the portion of soft tissue of the patient; andevaluating, using the one or more processors, an oblique plane normal tothe curve at the point where the oblique plane intersects the curve. 30.The system of claim 28, wherein the set of reformatted images is normalto the curve at the point where each reformatted image intersects thecurve.
 31. A system for locating anatomical landmarks in image data,comprising: a device configured to receive image data representative ofa patient's joint; one or more graphical user interface units configuredto: receive user inputs identifying a first point and a second pointalong a portion of soft tissue of the patient represented in the imagedata representative of the patient's joint; receive user inputs tracinga feature of the portion of soft tissue of the patient having alongitudinal axis; one or more processors coupled to the deviceconfigured to receive image data representative of a patient's joint andthe one or more graphical user interface units configured to: identifyone of a line or a curve that connects the first point and the secondpoint along a portion of soft tissue of the patient; identify a set ofplanes comprising (i) an oblique plane intersecting a normal to the lineor the curve and (ii) at least one other plane parallel to the obliqueplane; generate, using the image data representative of the patient'sjoint, a set of reformatted images by: receiving the location andorientation of the oblique plane; generating a set of oblique planesparallel or orthogonal to the oblique plane; sampling the image datarepresentative of the patient's joint along the set of oblique planes;computing the corners of the set of oblique planes by determining theintersection of each of the oblique planes in the set of oblique planeswith the image data representative of the patient's joint; generatingtexture coordinates using the corners of the set of the oblique planes;assigning pixel intensity values to the texture coordinates; andinterpolating pixel intensity values for texture coordinates that do notcoincide with points in the image data representative of the patient'sjoint; generate a 3-D model representative of the patient's joint usingthe set of reformatted images; generate a 3-D marking representing thetraced feature of the portion of soft tissue of the patient on the 3-Dmodel representative of the patient's joint; and one or more screenscoupled to the one or more graphical user interface units and the one ormore processors configured to display one or more of the image datarepresentative of the patient's joint, the first point and the secondpoint along the portion of soft tissue of the patient represented in theimage data representative of the patient's joint, the traced feature ofthe portion of soft tissue of the patient, the oblique plane, the 3-Dmodel representative of the patient's joint, one or more reformattedimages from the set of reformatted images, and the 3-D markingrepresenting the traced feature of the portion of soft tissue of thepatient;
 32. The system of claim 31, wherein the set of oblique planesspan the entire volume of the image data representative of the patient'sjoint.
 33. The system of claim 31, wherein the set of oblique planes areequally spaced.
 34. The system of claim 31, wherein the pixel intensityvalues represent radiodensity measurements, attenuation coefficients,tissue relaxation time, or proton density.
 35. The system of claim 31,wherein the interpolation is achieved using nearest-neighborinterpolation, bilinear interpolation, trilinear interpolation, cubicinterpolation, or any combination thereof.